Stereotactic ablative radiotherapy (SABR) is used to treat subjects with early stage non-small cell lung cancer (NSCLC) who are medically inoperable or refuse surgery1. SABR uses advanced treatment planning and delivery to treat tumors at a high dose, while sparing surrounding normal tissue. Multiple collimated radiation beams are used to achieve a dose distribution highly conformal to the shape of the tumor with steep dose gradients.
The imaging modality generally used for post-SABR follow up is computed tomography (CT). During follow-up assessment, a key clinical decision is whether to provide further, possibly more invasive intervention, such as for example surgery or chemotherapy, to treat or remove recurrent/residual disease. This clinical decision relies on the ability to assess the success of the SABR treatment, that is, to determine whether the subject's cancer will recur. Since recurrent lung cancer typically progresses quickly, a decision to proceed with further intervention is valuable if made early. Delayed detection of recurrence may reduce the options for salvage therapies. This clinical decision is complicated by the fact that following radiotherapy to the lung, radiation induced lung injury (RILI) may occur as radiation pneumonitis and radiation fibrosis which appear as an increase in lung density on CT2,3. Following treatment with SABR, RILI can have a similar size and morphology as a recurrent tumor4,5 thereby making it difficult to differentiate between the two. Several studies have looked at the radiologic appearance of recurrence on follow-up CT post-SABR, and suggest that an enlarging opacity twelve (12) months after treatment is most suggestive of recurrence6,7. These studies also suggest that other imaging features, such as a bulging margin and disappearance of air bronchograms, are also suggestive of recurrence8,9.
A means for predicting recurrence within six (6) months of treatment based on CT imaging would permit timely intervention for recurrence, which typically manifests after twelve (12) months. Radiomics, the extraction of a large number of quantitative image features such as size, shape and appearance, has been shown to have prognostic power in lung cancer18. Image texture analysis has been used for computer-aided diagnosis on lung CT, and second-order texture statistics based on grey-level co-occurrence matrices (GLCMs) have been shown to quantify lung abnormalities10,11.
It is therefore an object to provide a novel method and apparatus for analyzing three-dimensional image data of a target region of a subject.